{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:G2FP3ZHSB7ZYTK44ZWCSN5F35A","short_pith_number":"pith:G2FP3ZHS","schema_version":"1.0","canonical_sha256":"368afde4f20ff389ab9ccd8526f4bbe83e0fabef9da6cfbf3d8020e53229dbfb","source":{"kind":"arxiv","id":"2504.17219","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Variational Autoencoders with Smooth Robust Latent Encoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Hyomin Lee, Jongheon Jeong, Minseon Kim, Sangwon Jang, Sung Ju Hwang","submitted_at":"2025-04-24T03:17:57Z","abstract_excerpt":"Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predictive models, it has been overlooked for generative models due to concerns about potential fidelity degradation by the nature of trade-offs between performance and robustness. In this work, we challenge this presumption, introducing Smooth Robust Latent VAE (SRL-VAE), a novel adversarial training framew"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.17219","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T03:17:57Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"12adb0b0a87b904c3b185c24f67bbc060b76ece3405a81fb5b872e5ea7ec0cce","abstract_canon_sha256":"0d427593b5b4c5c49ef71c515e4fac887ea3026dd8fff6cd14a481ba79a82c61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:24.497117Z","signature_b64":"+Gy0A4oUdofqj35XlUidVPlW4NNkkZKKpylEefmxW2NnXgC3GQ/we9L+mJ2X1oZdUmcHSt5wU9Ixsl1QZuNyCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"368afde4f20ff389ab9ccd8526f4bbe83e0fabef9da6cfbf3d8020e53229dbfb","last_reissued_at":"2026-07-05T10:53:24.496578Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:24.496578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Variational Autoencoders with Smooth Robust Latent Encoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Hyomin Lee, Jongheon Jeong, Minseon Kim, Sangwon Jang, Sung Ju Hwang","submitted_at":"2025-04-24T03:17:57Z","abstract_excerpt":"Variational Autoencoders (VAEs) have played a key role in scaling up diffusion-based generative models, as in Stable Diffusion, yet questions regarding their robustness remain largely underexplored. Although adversarial training has been an established technique for enhancing robustness in predictive models, it has been overlooked for generative models due to concerns about potential fidelity degradation by the nature of trade-offs between performance and robustness. In this work, we challenge this presumption, introducing Smooth Robust Latent VAE (SRL-VAE), a novel adversarial training framew"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17219","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.17219/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.17219","created_at":"2026-07-05T10:53:24.496639+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17219v1","created_at":"2026-07-05T10:53:24.496639+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17219","created_at":"2026-07-05T10:53:24.496639+00:00"},{"alias_kind":"pith_short_12","alias_value":"G2FP3ZHSB7ZY","created_at":"2026-07-05T10:53:24.496639+00:00"},{"alias_kind":"pith_short_16","alias_value":"G2FP3ZHSB7ZYTK44","created_at":"2026-07-05T10:53:24.496639+00:00"},{"alias_kind":"pith_short_8","alias_value":"G2FP3ZHS","created_at":"2026-07-05T10:53:24.496639+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.12009","citing_title":"LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02751","citing_title":"Understanding Latent Diffusability via Fisher Geometry","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A","json":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A.json","graph_json":"https://pith.science/api/pith-number/G2FP3ZHSB7ZYTK44ZWCSN5F35A/graph.json","events_json":"https://pith.science/api/pith-number/G2FP3ZHSB7ZYTK44ZWCSN5F35A/events.json","paper":"https://pith.science/paper/G2FP3ZHS"},"agent_actions":{"view_html":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A","download_json":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A.json","view_paper":"https://pith.science/paper/G2FP3ZHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17219&json=true","fetch_graph":"https://pith.science/api/pith-number/G2FP3ZHSB7ZYTK44ZWCSN5F35A/graph.json","fetch_events":"https://pith.science/api/pith-number/G2FP3ZHSB7ZYTK44ZWCSN5F35A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A/action/storage_attestation","attest_author":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A/action/author_attestation","sign_citation":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A/action/citation_signature","submit_replication":"https://pith.science/pith/G2FP3ZHSB7ZYTK44ZWCSN5F35A/action/replication_record"}},"created_at":"2026-07-05T10:53:24.496639+00:00","updated_at":"2026-07-05T10:53:24.496639+00:00"}